Top Data Engineer Jobs in Omaha, NE

Design, build, and maintain reliable data pipelines and models using SQL, Python, and Databricks/Spark. Translate multi-source schemas into scalable data models, enforce data hygiene and privacy, add tests/documentation/lineage, and collaborate with analytics, product, marketing, and vendor teams to deliver trustworthy data for personalization and marketing.

Lead architecture and hands-on engineering to design, scale, and govern a cloud-native lakehouse using Databricks and Microsoft Fabric. Build batch/streaming pipelines, ensure HIPAA-compliant PHI handling, produce analytics-ready datasets, mentor engineers, and drive platform strategy, governance, and reliability.

Design, build, and own data infrastructure and ETL/ELT pipelines (Airflow, S3, Lambda) into a governed Snowflake warehouse. Implement data quality, IaC (Terraform/CDK), CI/CD, and analytics solutions (SQL, Tableau). Enable ML/LLM use cases like forecasting and anomaly detection and deliver automated reporting and data-driven marketing outputs.

Design, build, and maintain scalable data pipelines and tenant-aware APIs to ingest and transform data from varied sources. Work with event-driven and batch architectures, implement authentication and integration patterns, ensure observability/monitoring, optimize data flows for cost and performance, and collaborate with product, infrastructure, and engineering teams to onboard and maintain data sources at scale.

Design, build, and maintain data models, pipelines, and access layers to support care delivery, operations, and analytics. Ensure data quality, governance, and reliability while optimizing infrastructure and performance. Serve as a technical leader, review code, set engineering best practices, and collaborate cross-functionally to translate business needs into scalable data solutions.

Build and maintain scalable ETL pipelines using Python, Spark, and Airflow; collaborate with AI/ML and Product teams to deliver AI-native data products; identify and resolve ETL bottlenecks; ensure code quality through reviews and tests; own sprint deliverables and contribute to roadmap planning and major epics.

Seeking a Senior BODS Developer to develop and maintain complex BODS processes for data transformation. The role includes improving data quality using Information Steward and GAC/URAC techniques, collaborating with teams, creating documentation, and mentoring others.

The Senior Data Engineer will design and optimize data infrastructures and pipelines, lead projects, mentor juniors, and enforce best practices.

Design and implement Lakehouse solutions and data pipelines, optimize Databricks environments, and integrate AI applications while delivering exceptional results for clients.

The Sr. Data Engineer - Databricks designs and maintains scalable data solutions using Apache Spark and Databricks, ensuring data quality and performance for analytics and reporting.

The Senior Data Engineer will design and implement complex data models, collaborate on data governance, lead data initiatives, and mentor team members.

Design and optimize SQL Server solutions, manage data, automate processes with PowerShell, and integrate with external systems to enhance functionality.

The Data Engineer will design and maintain scalable data pipelines, automate workflows, and enhance data governance while collaborating with cross-functional teams.

The Senior Data Engineer will develop data engineering solutions, manage ETL workflows, and establish a new data practice while collaborating with stakeholders.

The Lead Data Platform Engineer will design and build Virtuous's data platform, ensuring it is secure, scalable, and AI-ready. Responsibilities include architecting access controls, optimizing performance, and enabling self-service analytics while maintaining governance and compliance standards.

The Data Architect will design scalable data models, transform raw data into actionable insights, and deploy analytics solutions for healthcare providers, with a strong emphasis on SQL proficiency and cloud technologies.

The Senior Data Engineer will design and scale cloud-based data platforms, ensuring quality and performance while collaborating with cross-functional teams to deliver actionable data solutions.

The Senior Data Engineer will build and maintain ETL/ELT pipelines, optimize SQL queries, and collaborate on machine learning workflows, driving data engineering best practices.

The Staff Data Engineer will lead financial data operations, ensuring accuracy and reliability of data pipelines, while collaborating with Finance, Product, and Engineering teams to enhance systems and decision-making processes.

Lead the data engineering team to design and maintain data pipelines, evaluate technologies, mentor juniors, and ensure system performance and reliability.

Design, build, and maintain scalable, secure data pipelines and platforms to acquire, transform, and store large financial datasets. Enable AI/GenAI-ready data foundations for model training, inference, retrieval, and RAG workflows. Collaborate with cross-functional teams, ensure data quality, governance, observability, and compliance, and support production deployments and troubleshooting.

Design, build, and own end-to-end data systems and pipelines powering payments, fraud response, and reporting. Implement streaming and batch integrations, APIs, and workflow orchestration. Define data correctness with monitoring, alerting, and tests. Create reusable patterns and collaborate with stakeholders to clarify requirements and tradeoffs.

Design and deliver scalable, reliable data services and microservices for real-time and batch processing. Build REST APIs, model data across SQL/NoSQL stores, drive data quality, own deployment/monitoring/incident response, set technical standards, and mentor engineers.

As a Data Engineer at Common Room, you'll build ETL pipelines, process billions of events daily, and collaborate on backend projects using AWS, Postgres, and Node.js.

Lead design, build, and optimization of Sezzle's large-scale data warehousing and pipelines. Own data infrastructure KPIs/SLAs, improve Redshift performance, evolve the data stack, implement ETL/ELT workflows, and partner with cross-functional teams to deliver reliable, scalable datasets.